<ul><li>Recurrent Neural Networks (RNNs) have loops in their architecture, allowing them to maintain a memory of past inputs.</li><li>RNNs struggle with handling long-term dependencies due to the vanishing gradient problem.</li><li>Another major challenge is unstable training caused by exploding gradients.</li><li>Long Short-Term Memory (LSTM) networks have been developed to address these issues and can selectively remember or forget information over long sequences.</li></ul>

The Problem with RNNs: Why We Moved to LSTMs

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